
** Clearing Stata memory
capture log close
clear all
set more off, perm
set seed 1234

///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////// Table 1: Priority and P1 Subject-Specific Performance ///////////////////////////////////////////////////////////////////////////////////
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////

** Opening Phase 2 norm_p1scores dataset 
use "Work Data/Gender_Phase2_long.dta",clear

*** Creating variables
encode subject, gen (sub)
tab subject, gen (d_sub)
label var sub "Subject"

** Subject dummies
rename d_sub1 Biology
rename d_sub2 Chemistry
rename d_sub3 Geography
rename d_sub4 History
rename d_sub5 Language
rename d_sub6 Mathematics
rename d_sub7 Physics
rename d_sub8 Portuguese
* Labels
label var Biology "Biology"
label var Chemistry "Chemistry"
label var Geography "Geography"
label var History "History"
label var Math "Mathematics"
label var Physics "Physics"
label var Portuguese "Portuguese"
label var Language "Foreign Language"

** Interaction: priority X female
gen fem_priority=female*priority
label var fem_priority "Female $\times$ Future priority"

** Interaction: priority X subject
gen fem_priority=female*priority
foreach v of varlist Biology-Portuguese {
gen fem_`v'=`v'*female
label var fem_`v' "Female $\times$ `v'"
gen prio_`v'=priority*`v'
label var prio_`v' "Future priority $\times$ `v'"
gen fem_prio_`v'=fem_priority*`v'
label var fem_prio_`v' "Female $\times$ Future priority $\times$ `v'"
}

global subject "Chemistry Geography History Mathematics Physics"
global subject_fem "fem_Chemistry fem_Geography fem_History fem_Mathematics fem_Physics"

*********************************************************************************
****************   Relative performances ****************************************
*********************************************************************************

******************************** ENEM ********************************

foreach v in norm_enem_w {
bys year female: egen `v'_ave_g=mean(`v')
gen `v'_g=`v'-`v'_ave_g
bys year female: sum `v'_g
}
label var norm_enem_w_g "ENEM"
drop norm_enem_w_ave_g

* Interaction: subject X ENEM
foreach v of varlist Biology-Portuguese {
gen enem_`v'=`v'*norm_enem_w_g
label var enem_`v' "ENEM scores $\times$ `v'"
gen fem_enem_`v'=female*norm_enem_w_g*`v'
label var fem_enem_`v' "Female $\times$ ENEM scores $\times$ `v'"
forvalues i=2(1)4 {
gen enem_`v'_`i'=enem_`v'^`i'
gen fem_enem_`v'_`i'=fem_enem_`v'^`i'
}
sum enem_`v'* fem_enem_`v'*
}

global g_pol_enem_sub "enem_Chemistry* enem_Geography* enem_History* enem_Mathematics* enem_Physics*"
d $g_pol_enem_sub

* Priority x relative performance in ENEM:
foreach v in  norm_enem_w {
gen `v'_priority_g=`v'_g*priority
forvalues i=2(1)4 {
gen `v'_priority_g`i'=`v'_g^`i'*priority
sum `v'_priority_g`i'
}
}

global g_norm_enem_w_prio norm_enem_w_priority_g*
d $g_norm_enem_w_prio

*********************************************************************************
**************** Main sample ****************************************************
*********************************************************************************

* 1) Only years before the affirmative action took place
drop if aa_year==1
drop if year==2000
tab year

* 2) Drop Portuguese and Foreign Language (in Phase 1 there is no Portuguese or Foreign Language exams - For Portuguese Phase 1 has an essay)
 tab subject, sum(norm_p1score)
 drop if subject=="lang" | subject=="port" 
 tab subject, sum(norm_p1score)
 drop Language Portuguese prio_Language prio_Portuguese fem_prio_Language fem_prio_Portuguese 

** Labeling variables
label var priority "Future priority"
label var female "Female"

estimates clear

reg norm_p1score female priority fem_priority norm_enem_w_g, cluster(inscri2) 
estimates store reg1
reg norm_p1score female priority fem_priority norm_enem_w_g $subject $subject_fem, cluster(inscri2) 
estimates store reg2
reghdfe norm_p1score priority fem_priority $subject $subject_fem , cluster(inscri2) absorb(inscri2)
estimates store reg3
reghdfe norm_p1score priority fem_priority $subject $subject_fem $g_pol_enem_sub, cluster(inscri2) absorb(inscri2)
estimates store reg4
reghdfe norm_p1score priority fem_priority $subject $subject_fem $g_pol_enem_sub $g_norm_enem_w_prio, cluster(inscri2) absorb(inscri2)
estimates store reg5

estadd local sub_fe "No":  reg1
estadd local sub_fe "Yes": reg2 reg3 reg4 reg5

estadd local subgender_fe "No":  reg1
estadd local subgender_fe "Yes": reg2 reg3 reg4 reg5

estadd local ind_fe "No": reg1 reg2  
estadd local ind_fe "Yes": reg3 reg4 reg5

estadd local enemsub "No":  reg1 reg2 reg3
estadd local enemsub "Yes":  reg4 reg5

estadd local enemprio_pol4 "No":  reg1 reg2 reg3 reg4
estadd local enemprio_pol4 "Yes": reg5 


* Tex
esttab reg1 reg2 reg3 reg4 reg5 using "Output/p_general_result_phase1score_sample.tex", se star(* 0.10 ** 0.05 *** 0.01) nogap ///
stats(r2_a N N_clust sep sub_fe subgender_fe ind_fe enemsub enemprio_pol4 , fmt(%9.3fc %9.0fc %9.0fc %1s %3s %3s %3s %3s) /// 
labels("$\bar{R}^2$" "Number of observations"  "Number of applicants" " " "Subject FE" "Subject-gender FE" /// 
 "Individual FE" "ENEM $\times$ Subject FE" "ENEM $\times$ Future Priority")) b(%7.3f) se(%7.3f)  booktabs replace f label nomtitle collabels(none) keep(female priority fem_priority norm_enem_w_g) ///
refcat(female " \\ \multicolumn{6}{l}{\textit{Dependent variable: Phase 1 normalized subject-specific scores}} \\", nolabel)




